· 2015
SUN RGB-D: A RGB-D scene understanding benchmark suite
- 1,902citations
- 2015year
Short summary
A new benchmark suite, SUN RGB-D, is introduced for 3D scene understanding, featuring 10,335 images with dense 2D and 3D annotations, including object orientations and room layouts.
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Key points
- Introduces SUN RGB-D, a large-scale benchmark suite for RGB-D scene understanding.
- Contains 10,335 RGB-D images with dense 2D and 3D annotations.
- Includes 146,617 2D polygons and 64,595 3D bounding boxes with object orientations.
- Provides 3D room layout and scene category for each image.
- Enables training of data-hungry algorithms and evaluation using 3D metrics.
AI-generated from the title and abstract; the full text is not read.
Abstract
Although RGB-D sensors have enabled major break-throughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this paper, we introduce an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,335 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 64,595 3D bounding boxes with accurate object orientations, as well as a 3D room layout and scene category for each image. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.
The authors' abstract, as published at the source., 2015 · DOI ↗
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Field: Aerospace Engineering
Aerospace EngineeringEngineering